Code
library(MASS)
library(stargazer)
library(httr)
library(jsonlite)
library(knitr)
library(rgbif)
library(ggrepel)
library(extrafont)
library(tidyverse)
options(knitr.kable.NA = '')
options(scipen=999)Data analyses
The iNaturalist Network is a localised experience that is fully connected to the global iNaturalist community. Network members are local institutions that promote local use and facilitate the use of data from iNaturalist to benefit local
The aim of this report is to give an account of the importance of the iNaturalist network members by analysing the number of records for each country.
library(MASS)
library(stargazer)
library(httr)
library(jsonlite)
library(knitr)
library(rgbif)
library(ggrepel)
library(extrafont)
library(tidyverse)
options(knitr.kable.NA = '')
options(scipen=999)iNat_network <-
tribble(~'site', ~'site_name', ~'site_id',
'Global', 'iNaturalist', 1,
'Mexico', 'iNaturalistMX', 2,
'New Zealand', 'iNaturalistNZ', 3,
'Canada', 'iNaturalist.ca', 5,
'Colombia', 'NaturalistaCO', 6,
'Portugal', 'BioDiversity4All', 8,
'Australia', 'iNaturalistAU', 9,
'Panama', 'iNaturalistPa', 13,
'Ecuador', 'iNaturalistEc', 14,
'Israel', 'iNaturalistil', 15,
'Argentina', 'ArgentiNat', 16,
'Costa Rica', 'NaturalistaCR', 17,
'Chile', 'iNaturalistCL', 18,
'Finland', 'iNaturalistFi', 20,
'Sweeden', 'iNaturalist.Se', 21,
'Spain', 'Natusfera', 22,
'Greece', 'iNaturalistGR', 23,
'Guatemala', 'iNaturalistGT', 24,
'United Kingdom', 'iNaturalistUK', 25,
'Luxembourg', 'iNaturalist.LU', 26,
'Taiwan', 'iNaturalistTW', 27,
'Uruguay', 'NaturalistaUY', 28)
iNat_network %>%
mutate('#'= row_number()) %>% relocate('#') %>%
rename(`Site` = site,
`Name`=site_name,
`ID`=site_id) %>%
kableExtra::kbl(digits=1, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(row = c(2,5,8,9,11,12,13,18,22), bold = T, color = "white", background = "#228A22")| # | Site | Name | ID |
|---|---|---|---|
| 1 | Global | iNaturalist | 1 |
| 2 | Mexico | iNaturalistMX | 2 |
| 3 | New Zealand | iNaturalistNZ | 3 |
| 4 | Canada | iNaturalist.ca | 5 |
| 5 | Colombia | NaturalistaCO | 6 |
| 6 | Portugal | BioDiversity4All | 8 |
| 7 | Australia | iNaturalistAU | 9 |
| 8 | Panama | iNaturalistPa | 13 |
| 9 | Ecuador | iNaturalistEc | 14 |
| 10 | Israel | iNaturalistil | 15 |
| 11 | Argentina | ArgentiNat | 16 |
| 12 | Costa Rica | NaturalistaCR | 17 |
| 13 | Chile | iNaturalistCL | 18 |
| 14 | Finland | iNaturalistFi | 20 |
| 15 | Sweeden | iNaturalist.Se | 21 |
| 16 | Spain | Natusfera | 22 |
| 17 | Greece | iNaturalistGR | 23 |
| 18 | Guatemala | iNaturalistGT | 24 |
| 19 | United Kingdom | iNaturalistUK | 25 |
| 20 | Luxembourg | iNaturalist.LU | 26 |
| 21 | Taiwan | iNaturalistTW | 27 |
| 22 | Uruguay | NaturalistaUY | 28 |
We tested different explanatory variables and see which is the model that best explains the total number of records a country has on iNaturalist.
Response variables:
n_records_inat.n_records_gbif_iNat.n_users.Explanatory variables:
population.area.latitude.hdi.gdp_per_capita.gdp_research.Functions
source('R/national_sites.R')Data download
LatinAmerica <- tibble(country_name= c('Mexico', 'Brazil', 'Costa Rica', 'Colombia', 'Peru', 'Argentina', 'Ecuador', 'Panama', 'Chile', 'Venezuela', 'Belize', 'Honduras', 'Bolivia', 'Guatemala', 'Cuba', 'Nicaragua', 'Paraguay', 'Bahamas', 'Jamaica', 'Trinidad and Tobago', 'Guyana', 'Dominican Republic', 'El Salvador', 'Suriname', 'Uruguay', 'Haiti'))
LatinAmerica <- LatinAmerica %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
LatinAmerica <- left_join(LatinAmerica, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(LatinAmerica$country_code)
n_inat_country <- recordsPerCountryiNat(LatinAmerica$country_name)
n_users_country <- usersPerCountryiNat(LatinAmerica$country_name)
area_country <-areaPerCountry(LatinAmerica$country_code)
population <- populationPerCountry(LatinAmerica$country_code)
gdp_per_capita <- gdpPerCapitaCountry(LatinAmerica$country_code)
gdp_research <- gdpResearchPerCountry(LatinAmerica$country_code)
latitude <- latitudePerCountry(LatinAmerica$country_code)
data_variables_Latam <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
LatinAmerica, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
saveRDS(data_variables_Latam, 'data/Latin_America_data_variables.rds')
########################################################################
Europe <- tibble(country_name = c('Austria', 'Belgium', 'Bulgaria', 'Croatia', 'Cyprus', 'Czechia', 'Denmark', 'Estonia', 'Finland', 'France', 'Germany', 'Greece', 'Hungary', 'Ireland', 'Italy', 'Latvia', 'Lithuania', 'Luxembourg', 'Malta', 'Netherlands', 'Poland', 'Portugal', 'Romania', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'United Kingdom', 'Iceland', 'Liechtenstein', 'Norway', 'Switzerland', 'Albania', 'Bosnia and Herzegovina', 'Georgia', 'Moldova', 'Montenegro', 'Macedonia', 'Serbia', 'Turkey', 'Ukraine'))
Europe <- Europe %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
Europe <- left_join(Europe, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(Europe$country_code)
n_inat_country <- recordsPerCountryiNat(Europe$country_name)
n_users_country <- usersPerCountryiNat(Europe$country_name)
area_country <-areaPerCountry(Europe$country_code)
population <- populationPerCountry(Europe$country_code)
gdp_per_capita <- gdpPerCapitaCountry(Europe$country_code)
gdp_research <- gdpResearchPerCountry(Europe$country_code)
latitude <- latitudePerCountry(Europe$country_code)
data_variables_Europe <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
Europe, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
saveRDS(data_variables_Europe, 'data/Europe_data_variables.rds')
########################################################################
Asia <- tibble(country_name = c('India', 'China', 'Indonesia', 'Pakistan', 'Bangladesh', 'Japan', 'Philippines', 'Vietnam', 'Iran', 'Turkey', 'Thailand', 'Myanmar', 'South Korea','Iraq', 'Afghanistan', 'Yemen', 'Uzbekistan', 'Malaysia', 'Saudi Arabia', 'Nepal', 'North Korea','Syria', 'Sri Lanka','Kazakhstan', 'Cambodia', 'Jordan', 'United Arab Emirates', 'Tajikistan', 'Azerbaijan', 'Israel', 'Laos', 'Turkmenistan', 'Kyrgyzstan', 'Singapore', 'Lebanon', 'Palestine','Oman', 'Kuwait', 'Georgia', 'Mongolia', 'Qatar', 'Armenia', 'Bahrain', 'Timor Leste', 'Cyprus', 'Bhutan', 'Maldives', 'Brunei', 'Taiwan'))
Asia <- Asia %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
Asia <- left_join(Asia, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(Asia$country_code)
n_inat_country <- recordsPerCountryiNat(Asia$country_name)
n_users_country <- usersPerCountryiNat(Asia$country_name)
area_country <-areaPerCountry(Asia$country_code)
population <- populationPerCountry(Asia$country_code)
gdp_per_capita <- gdpPerCapitaCountry(Asia$country_code)
gdp_research <- gdpResearchPerCountry(Asia$country_code)
latitude <- latitudePerCountry(Asia$country_code)
data_variables_Asia <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
Asia, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
data_variables_Asia <- data_variables_Asia %>%
mutate(area = ifelse(country_name == 'Taiwan', 36197, area),
pop = ifelse(country_name == 'Taiwan', 23365274, pop))
saveRDS(data_variables_Asia, 'data/Asia_data_variables.rds')
########################################################################
Oceania <- tibble(country_name = c('Australia', 'Papua New Guinea', 'New Zealand', 'Fiji', 'Solomon Islands', 'Federated States of Micronesia', 'Vanuatu', 'Samoa', 'Kiribati', 'Tonga', 'Marshall Islands', 'Palau', 'Tuvalu', 'Nauru'))
Oceania <- Oceania %>%
mutate(country_code = countrycode::countrycode(country_name,
origin = 'country.name',
destination = 'iso2c'))
Oceania <- left_join(Oceania, iNat_network %>% rename(country_name=site))
n_inat_gbif_country <- recordsPerCountryGBIF(Oceania$country_code)
n_inat_country <- recordsPerCountryiNat(Oceania$country_name)
n_users_country <- usersPerCountryiNat(Oceania$country_name)
area_country <-areaPerCountry(Oceania$country_code)
population <- populationPerCountry(Oceania$country_code)
gdp_per_capita <- gdpPerCapitaCountry(Oceania$country_code)
gdp_research <- gdpResearchPerCountry(Oceania$country_code)
latitude <- latitudePerCountry(Oceania$country_code)
data_variables_Oceania <- left_join(left_join(left_join(
left_join(left_join(left_join(left_join(left_join(
Oceania, n_inat_gbif_country),
n_inat_country),
n_users_country),
area_country),
population),
gdp_per_capita),
gdp_research), latitude)
saveRDS(data_variables_Oceania, 'data/Oceania_data_variables.rds')
########################################################################
variables_global <- bind_rows(data_variables_Latam %>%
mutate(continent = 'America'),
data_variables_Europe %>%
mutate(continent = 'Europe'),
data_variables_Asia %>%
mutate(continent = 'Asia'),
data_variables_Oceania %>%
mutate(continent = 'Oceania')) %>%
unique()
# variables_global <- bind_rows(readRDS('data/Latin_America_data_variables.rds') %>%
# mutate(continent = 'America'),
# readRDS('data/Europe_data_variables.rds') %>%
# mutate(continent = 'Europe'),
# readRDS('data/Asia_data_variables.rds') %>%
# mutate(continent = 'Asia'),
# readRDS('data/Oceania_data_variables.rds') %>%
# mutate(continent = 'Oceania')) %>%
# unique()
saveRDS(variables_global, 'data/global_data_variables.rds')data_variables %>%
select(country_name,site_name, n_records_inat) %>%
arrange(desc(n_records_inat)) %>%
mutate('#'= row_number()) %>% relocate('#') %>%
rename(`Country` = country_name,
`iNat site` = site_name,
`Records on iNa`=n_records_inat) %>%
kableExtra::kbl(digits=4, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(c(1,2,3,6,7,11,13,14,15,16,19,23,31,34,35,43,47,56,59), bold = T, color = "white", background = "#228A22") %>%
kableExtra::scroll_box(height = '600px')| # | Country | iNat site | Records on iNa |
|---|---|---|---|
| 1 | Australia | iNaturalistAU | 10,849,285 |
| 2 | Mexico | iNaturalistMX | 8,412,878 |
| 3 | United Kingdom | iNaturalistUK | 6,943,926 |
| 4 | Germany | 5,091,498 | |
| 5 | France | 4,932,213 | |
| 6 | Spain | Natusfera | 4,604,659 |
| 7 | Taiwan | iNaturalistTW | 3,666,074 |
| 8 | Italy | 3,662,680 | |
| 9 | India | 3,571,023 | |
| 10 | Brazil | 3,496,383 | |
| 11 | New Zealand | iNaturalistNZ | 2,981,047 |
| 12 | Austria | 2,476,537 | |
| 13 | Portugal | BioDiversity4All | 2,107,143 |
| 14 | Colombia | NaturalistaCO | 1,906,978 |
| 15 | Ecuador | iNaturalistEc | 1,874,298 |
| 16 | Argentina | ArgentiNat | 1,833,690 |
| 17 | Denmark | 1,657,667 | |
| 18 | China | 1,646,241 | |
| 19 | Costa Rica | NaturalistaCR | 1,580,750 |
| 20 | Ukraine | 1,526,147 | |
| 21 | Czechia | 1,301,754 | |
| 22 | Poland | 1,254,562 | |
| 23 | Finland | iNaturalistFi | 1,170,974 |
| 24 | Thailand | 996,118 | |
| 25 | Malaysia | 973,288 | |
| 26 | Japan | 965,864 | |
| 27 | Indonesia | 955,846 | |
| 28 | Netherlands | 954,360 | |
| 29 | Bolivia | 940,987 | |
| 30 | Switzerland | 905,895 | |
| 31 | Chile | iNaturalistCL | 874,637 |
| 32 | Peru | 785,040 | |
| 33 | Singapore | 746,405 | |
| 34 | Panama | iNaturalistPa | 716,233 |
| 35 | Greece | iNaturalistGR | 709,163 |
| 36 | Sweden | 659,853 | |
| 37 | Belgium | 647,042 | |
| 38 | South Korea | 532,394 | |
| 39 | Hungary | 481,094 | |
| 40 | Philippines | 479,525 | |
| 41 | Lithuania | 464,960 | |
| 42 | Croatia | 427,593 | |
| 43 | Luxembourg | iNaturalist.LU | 375,167 |
| 44 | Norway | 371,783 | |
| 45 | Turkey | 367,680 | |
| 46 | Turkey | 367,680 | |
| 47 | Israel | iNaturalistil | 358,153 |
| 48 | Honduras | 333,488 | |
| 49 | Ireland | 263,299 | |
| 50 | Romania | 247,772 | |
| 51 | Slovakia | 226,580 | |
| 52 | Slovenia | 205,635 | |
| 53 | Sri Lanka | 203,493 | |
| 54 | Kazakhstan | 198,800 | |
| 55 | Vietnam | 189,863 | |
| 56 | Guatemala | iNaturalistGT | 169,461 |
| 57 | Bulgaria | 162,215 | |
| 58 | Belize | 159,060 | |
| 59 | Uruguay | NaturalistaUY | 156,542 |
| 60 | Dominican Republic | 145,874 | |
| 61 | Serbia | 144,065 | |
| 62 | Trinidad and Tobago | 127,651 | |
| 63 | Nicaragua | 122,372 | |
| 64 | Iceland | 115,563 | |
| 65 | Nepal | 110,868 | |
| 66 | Mongolia | 94,205 | |
| 67 | El Salvador | 92,398 | |
| 68 | Venezuela | 89,283 | |
| 69 | Cuba | 87,891 | |
| 70 | Cambodia | 83,930 | |
| 71 | Estonia | 75,483 | |
| 72 | Jamaica | 73,803 | |
| 73 | Albania | 73,194 | |
| 74 | Armenia | 73,171 | |
| 75 | United Arab Emirates | 71,277 | |
| 76 | Latvia | 69,627 | |
| 77 | Fiji | 66,619 | |
| 78 | Cyprus | 65,867 | |
| 79 | Maldives | 61,266 | |
| 80 | Bahamas | 59,703 | |
| 81 | Iran | 59,489 | |
| 82 | Montenegro | 57,086 | |
| 83 | Bhutan | 46,283 | |
| 84 | Uzbekistan | 42,320 | |
| 85 | Bosnia and Herzegovina | 41,590 | |
| 86 | Pakistan | 36,960 | |
| 87 | Paraguay | 35,836 | |
| 88 | Bangladesh | 35,612 | |
| 89 | Papua New Guinea | 35,464 | |
| 90 | Kyrgyzstan | 33,781 | |
| 91 | Myanmar | 32,490 | |
| 92 | Palestine | 31,040 | |
| 93 | Saudi Arabia | 30,342 | |
| 94 | Oman | 29,897 | |
| 95 | Suriname | 29,310 | |
| 96 | Kuwait | 29,189 | |
| 97 | Malta | 27,991 | |
| 98 | Guyana | 27,703 | |
| 99 | Macedonia | 27,626 | |
| 100 | Laos | 26,952 | |
| 101 | Syria | 24,597 | |
| 102 | Marshall Islands | 24,568 | |
| 103 | Vanuatu | 23,271 | |
| 104 | Jordan | 22,029 | |
| 105 | Palau | 18,512 | |
| 106 | Azerbaijan | 18,467 | |
| 107 | Haiti | 16,634 | |
| 108 | Solomon Islands | 14,516 | |
| 109 | Moldova | 14,322 | |
| 110 | Iraq | 14,125 | |
| 111 | Lebanon | 13,122 | |
| 112 | Brunei | 11,386 | |
| 113 | Cyprus | 10,894 | |
| 114 | Qatar | 10,678 | |
| 115 | Yemen | 9,808 | |
| 116 | Tajikistan | 9,560 | |
| 117 | Tonga | 8,175 | |
| 118 | Georgia | 6,820 | |
| 119 | Georgia | 6,820 | |
| 120 | Federated States of Micronesia | 6,153 | |
| 121 | Samoa | 5,247 | |
| 122 | Liechtenstein | 4,717 | |
| 123 | Bahrain | 3,592 | |
| 124 | Tuvalu | 2,885 | |
| 125 | Kiribati | 2,550 | |
| 126 | North Korea | 1,884 | |
| 127 | Afghanistan | 1,050 | |
| 128 | Turkmenistan | 626 | |
| 129 | Nauru | 103 | |
| 130 | Timor Leste |
data_variables %>%
select(country_name, site_name, n_records_gbif, n_records_gbif_iNat) %>%
mutate(proportion=n_records_gbif_iNat*100/n_records_gbif) %>%
arrange(desc(proportion)) %>%
mutate('#'= row_number()) %>% relocate('#') %>%
select(-n_records_gbif_iNat) %>%
rename(`Country` = country_name,
`iNat site` = site_name,
`Records from iNat on GBIF`=n_records_gbif,
`Proportion`=proportion) %>%
kableExtra::kbl(digits=4, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(c(17,19,26,34,38,45,51,52,58,62,78,85,88,104,105,108,111,113,114,120), bold = T, color = "white", background = "#228A22") %>%
kableExtra::scroll_box(height = '600px')| # | Country | iNat site | Records from iNat on GBIF | Proportion |
|---|---|---|---|---|
| 1 | Maldives | 102,600 | 28.5780 | |
| 2 | Ukraine | 3,305,090 | 27.4630 | |
| 3 | Albania | 113,283 | 26.0198 | |
| 4 | Italy | 7,401,549 | 22.7511 | |
| 5 | Singapore | 1,664,886 | 22.4301 | |
| 6 | Kazakhstan | 435,373 | 22.2200 | |
| 7 | Montenegro | 142,401 | 19.2155 | |
| 8 | Tuvalu | 9,554 | 18.7565 | |
| 9 | Croatia | 1,078,021 | 18.6975 | |
| 10 | Malta | 81,373 | 18.3243 | |
| 11 | Lithuania | 1,232,602 | 17.2011 | |
| 12 | Bosnia and Herzegovina | 82,290 | 16.6764 | |
| 13 | Uzbekistan | 117,367 | 15.7259 | |
| 14 | Fiji | 275,858 | 15.1440 | |
| 15 | Marshall Islands | 109,599 | 14.1853 | |
| 16 | Hungary | 1,882,300 | 12.8489 | |
| 17 | New Zealand | iNaturalistNZ | 14,957,900 | 12.3202 |
| 18 | Czechia | 4,357,996 | 12.2621 | |
| 19 | Greece | iNaturalistGR | 3,065,179 | 12.0781 |
| 20 | Indonesia | 3,076,242 | 11.8549 | |
| 21 | Austria | 12,772,943 | 11.5651 | |
| 22 | Timor Leste | 91,370 | 11.3823 | |
| 23 | Slovenia | 790,292 | 11.2033 | |
| 24 | Armenia | 192,968 | 11.1687 | |
| 25 | Romania | 1,070,706 | 10.6347 | |
| 26 | Mexico | iNaturalistMX | 31,246,257 | 10.4663 |
| 27 | Iraq | 72,541 | 8.8419 | |
| 28 | Macedonia | 118,106 | 8.1918 | |
| 29 | Malaysia | 3,341,240 | 8.0139 | |
| 30 | Mongolia | 571,726 | 7.9358 | |
| 31 | Cyprus | 522,317 | 7.5020 | |
| 32 | Cyprus | 522,489 | 7.4995 | |
| 33 | Bahrain | 22,772 | 7.4697 | |
| 34 | Argentina | ArgentiNat | 14,442,374 | 7.3516 |
| 35 | Serbia | 865,944 | 7.2042 | |
| 36 | Dominican Republic | 784,391 | 7.1786 | |
| 37 | Slovakia | 1,688,147 | 7.0103 | |
| 38 | Taiwan | iNaturalistTW | 21,421,162 | 6.9228 |
| 39 | Kyrgyzstan | 203,073 | 6.7690 | |
| 40 | Yemen | 91,962 | 6.7506 | |
| 41 | Vietnam | 783,215 | 6.7150 | |
| 42 | Jordan | 140,953 | 6.5376 | |
| 43 | Brunei | 49,391 | 6.4060 | |
| 44 | Vanuatu | 165,949 | 6.0513 | |
| 45 | Luxembourg | iNaturalist.LU | 3,354,337 | 5.9892 |
| 46 | Philippines | 2,079,909 | 5.8888 | |
| 47 | Thailand | 6,122,726 | 5.7396 | |
| 48 | Japan | 8,472,177 | 5.1061 | |
| 49 | Palau | 193,504 | 5.1053 | |
| 50 | Solomon Islands | 185,161 | 5.0497 | |
| 51 | Portugal | BioDiversity4All | 19,715,797 | 5.0231 |
| 52 | Uruguay | NaturalistaUY | 1,681,236 | 4.9915 |
| 53 | China | 9,433,222 | 4.9189 | |
| 54 | Bulgaria | 1,835,892 | 4.7743 | |
| 55 | Samoa | 45,966 | 4.7731 | |
| 56 | Sri Lanka | 2,146,804 | 4.4949 | |
| 57 | Trinidad and Tobago | 1,127,557 | 4.4920 | |
| 58 | Ecuador | iNaturalistEc | 11,650,103 | 4.4398 |
| 59 | Georgia | 1,181,861 | 4.3536 | |
| 60 | Georgia | 1,181,992 | 4.3531 | |
| 61 | Germany | 62,252,275 | 4.3359 | |
| 62 | Australia | iNaturalistAU | 135,376,182 | 4.1283 |
| 63 | Turkey | 3,309,188 | 4.1221 | |
| 64 | Turkey | 3,309,436 | 4.1218 | |
| 65 | Bolivia | 2,200,010 | 4.1167 | |
| 66 | Myanmar | 333,389 | 4.0925 | |
| 67 | Poland | 14,780,502 | 4.0749 | |
| 68 | Latvia | 604,673 | 4.0703 | |
| 69 | Jamaica | 680,756 | 3.9274 | |
| 70 | Moldova | 121,784 | 3.8347 | |
| 71 | Haiti | 189,175 | 3.7553 | |
| 72 | Brazil | 26,616,459 | 3.7527 | |
| 73 | Kuwait | 311,359 | 3.6989 | |
| 74 | Qatar | 135,904 | 3.6460 | |
| 75 | South Korea | 5,403,467 | 3.6406 | |
| 76 | Laos | 242,832 | 3.5642 | |
| 77 | Iran | 818,764 | 3.4275 | |
| 78 | Chile | iNaturalistCL | 10,730,491 | 3.3229 |
| 79 | Azerbaijan | 242,165 | 3.2565 | |
| 80 | Honduras | 3,459,123 | 3.2295 | |
| 81 | Tonga | 133,783 | 3.1147 | |
| 82 | Lebanon | 147,284 | 3.0723 | |
| 83 | Tajikistan | 84,825 | 3.0380 | |
| 84 | Bahamas | 987,306 | 3.0325 | |
| 85 | Spain | Natusfera | 72,566,816 | 2.9929 |
| 86 | Cambodia | 950,623 | 2.9868 | |
| 87 | Syria | 310,449 | 2.9493 | |
| 88 | Panama | iNaturalistPa | 7,850,521 | 2.9067 |
| 89 | Cuba | 1,845,461 | 2.8590 | |
| 90 | Palestine | 581,583 | 2.8202 | |
| 91 | Suriname | 463,888 | 2.7455 | |
| 92 | Saudi Arabia | 501,270 | 2.7037 | |
| 93 | Nicaragua | 2,045,925 | 2.6963 | |
| 94 | North Korea | 47,941 | 2.6950 | |
| 95 | El Salvador | 1,250,974 | 2.6841 | |
| 96 | Iceland | 2,138,710 | 2.6692 | |
| 97 | Liechtenstein | 86,720 | 2.5023 | |
| 98 | Nepal | 1,290,694 | 2.4486 | |
| 99 | Oman | 523,644 | 2.4125 | |
| 100 | Peru | 8,881,611 | 2.3764 | |
| 101 | Ireland | 5,073,295 | 2.2550 | |
| 102 | Bhutan | 564,497 | 2.2252 | |
| 103 | Bangladesh | 653,738 | 2.0881 | |
| 104 | United Kingdom | iNaturalistUK | 179,386,804 | 2.0854 |
| 105 | Israel | iNaturalistil | 7,023,671 | 2.0637 |
| 106 | India | 50,945,385 | 2.0362 | |
| 107 | Federated States of Micronesia | 130,557 | 2.0068 | |
| 108 | Costa Rica | NaturalistaCR | 31,581,985 | 1.8071 |
| 109 | Switzerland | 28,174,048 | 1.6532 | |
| 110 | Pakistan | 572,636 | 1.5437 | |
| 111 | Colombia | NaturalistaCO | 32,226,616 | 1.4854 |
| 112 | United Arab Emirates | 1,968,642 | 1.4806 | |
| 113 | Guatemala | iNaturalistGT | 4,598,258 | 1.2107 |
| 114 | Guyana | 830,244 | 1.2107 | |
| 115 | France | 192,598,919 | 1.2021 | |
| 116 | Kiribati | 157,257 | 1.1618 | |
| 117 | Belize | 6,646,850 | 1.0838 | |
| 118 | Turkmenistan | 22,062 | 1.0742 | |
| 119 | Denmark | 60,344,753 | 1.0540 | |
| 120 | Finland | iNaturalistFi | 45,743,721 | 1.0521 |
| 121 | Papua New Guinea | 1,704,726 | 0.8613 | |
| 122 | Nauru | 4,714 | 0.8061 | |
| 123 | Venezuela | 4,189,542 | 0.7974 | |
| 124 | Belgium | 39,976,677 | 0.7507 | |
| 125 | Paraguay | 1,412,769 | 0.7230 | |
| 126 | Afghanistan | 65,829 | 0.6806 | |
| 127 | Estonia | 7,446,938 | 0.5402 | |
| 128 | Netherlands | 123,944,227 | 0.3671 | |
| 129 | Norway | 53,254,830 | 0.3433 | |
| 130 | Sweden | 141,826,712 | 0.2148 |
data_variables %>%
select(country_name, site_name, n_users) %>%
arrange(desc(n_users)) %>%
mutate('#'= row_number()) %>% relocate('#') %>%
rename(`Country` = country_name,
`iNat site` = site_name,
`Users recording on iNat`=n_users) %>%
kableExtra::kbl(digits=4, format.args = list(big.mark = ',')) %>%
kableExtra::kable_material('striped') %>%
kableExtra::row_spec(c(1,2,4,7,10,11,12,14,15,16,18,22,26,28,33,47,49,51,61), bold = T, color = "white", background = "#228A22") %>%
kableExtra::scroll_box(height = '600px')| # | Country | iNat site | Users recording on iNat |
|---|---|---|---|
| 1 | Mexico | iNaturalistMX | 166,175 |
| 2 | United Kingdom | iNaturalistUK | 145,086 |
| 3 | France | 128,279 | |
| 4 | Australia | iNaturalistAU | 116,817 |
| 5 | Italy | 85,798 | |
| 6 | Germany | 79,925 | |
| 7 | Spain | Natusfera | 75,563 |
| 8 | Denmark | 74,527 | |
| 9 | Brazil | 69,957 | |
| 10 | Taiwan | iNaturalistTW | 57,783 |
| 11 | Colombia | NaturalistaCO | 54,064 |
| 12 | New Zealand | iNaturalistNZ | 46,991 |
| 13 | India | 46,458 | |
| 14 | Portugal | BioDiversity4All | 36,431 |
| 15 | Ecuador | iNaturalistEc | 36,342 |
| 16 | Costa Rica | NaturalistaCR | 35,327 |
| 17 | Czechia | 33,922 | |
| 18 | Finland | iNaturalistFi | 29,066 |
| 19 | Netherlands | 29,007 | |
| 20 | Bolivia | 28,956 | |
| 21 | Austria | 28,649 | |
| 22 | Argentina | ArgentiNat | 24,765 |
| 23 | Belgium | 24,564 | |
| 24 | Japan | 23,220 | |
| 25 | Thailand | 22,958 | |
| 26 | Chile | iNaturalistCL | 22,833 |
| 27 | Switzerland | 21,953 | |
| 28 | Greece | iNaturalistGR | 21,690 |
| 29 | Indonesia | 21,514 | |
| 30 | Sweden | 19,000 | |
| 31 | Malaysia | 18,659 | |
| 32 | China | 17,797 | |
| 33 | Panama | iNaturalistPa | 17,553 |
| 34 | Poland | 17,070 | |
| 35 | Peru | 16,732 | |
| 36 | Philippines | 15,739 | |
| 37 | Turkey | 13,594 | |
| 38 | Turkey | 13,594 | |
| 39 | Croatia | 12,772 | |
| 40 | Ireland | 12,725 | |
| 41 | Norway | 12,583 | |
| 42 | Ukraine | 12,060 | |
| 43 | Singapore | 9,985 | |
| 44 | Lithuania | 9,104 | |
| 45 | Honduras | 8,713 | |
| 46 | South Korea | 7,949 | |
| 47 | Guatemala | iNaturalistGT | 7,814 |
| 48 | Hungary | 7,081 | |
| 49 | Luxembourg | iNaturalist.LU | 6,843 |
| 50 | Iceland | 6,417 | |
| 51 | Israel | iNaturalistil | 6,374 |
| 52 | Slovenia | 6,300 | |
| 53 | Romania | 6,116 | |
| 54 | Dominican Republic | 6,106 | |
| 55 | Vietnam | 6,037 | |
| 56 | Slovakia | 5,431 | |
| 57 | Belize | 4,653 | |
| 58 | Sri Lanka | 4,259 | |
| 59 | United Arab Emirates | 4,161 | |
| 60 | Bahamas | 4,158 | |
| 61 | Uruguay | NaturalistaUY | 4,041 |
| 62 | Bulgaria | 3,473 | |
| 63 | Nepal | 3,177 | |
| 64 | Jamaica | 3,138 | |
| 65 | Nicaragua | 2,966 | |
| 66 | Kazakhstan | 2,958 | |
| 67 | El Salvador | 2,831 | |
| 68 | Cuba | 2,707 | |
| 69 | Estonia | 2,686 | |
| 70 | Cyprus | 2,437 | |
| 71 | Serbia | 2,409 | |
| 72 | Cambodia | 2,407 | |
| 73 | Trinidad and Tobago | 2,326 | |
| 74 | Latvia | 2,273 | |
| 75 | Venezuela | 2,197 | |
| 76 | Montenegro | 2,118 | |
| 77 | Albania | 1,842 | |
| 78 | Malta | 1,672 | |
| 79 | Fiji | 1,650 | |
| 80 | Maldives | 1,568 | |
| 81 | Pakistan | 1,509 | |
| 82 | Bhutan | 1,452 | |
| 83 | Bosnia and Herzegovina | 1,404 | |
| 84 | Iran | 1,404 | |
| 85 | Armenia | 1,339 | |
| 86 | Saudi Arabia | 1,330 | |
| 87 | Paraguay | 1,280 | |
| 88 | Myanmar | 1,260 | |
| 89 | Jordan | 1,255 | |
| 90 | Laos | 1,254 | |
| 91 | Mongolia | 1,241 | |
| 92 | Macedonia | 1,100 | |
| 93 | Palestine | 1,093 | |
| 94 | Oman | 1,041 | |
| 95 | Uzbekistan | 969 | |
| 96 | Bangladesh | 904 | |
| 97 | Kyrgyzstan | 898 | |
| 98 | Lebanon | 703 | |
| 99 | Azerbaijan | 702 | |
| 100 | Papua New Guinea | 655 | |
| 101 | Guyana | 598 | |
| 102 | Cyprus | 532 | |
| 103 | Suriname | 485 | |
| 104 | Vanuatu | 478 | |
| 105 | Qatar | 461 | |
| 106 | Haiti | 436 | |
| 107 | Palau | 414 | |
| 108 | Moldova | 388 | |
| 109 | Iraq | 377 | |
| 110 | Liechtenstein | 375 | |
| 111 | Brunei | 346 | |
| 112 | Federated States of Micronesia | 345 | |
| 113 | Kuwait | 326 | |
| 114 | Tajikistan | 300 | |
| 115 | Georgia | 293 | |
| 116 | Georgia | 293 | |
| 117 | Bahrain | 291 | |
| 118 | Solomon Islands | 255 | |
| 119 | Samoa | 202 | |
| 120 | Syria | 172 | |
| 121 | Tonga | 168 | |
| 122 | Yemen | 129 | |
| 123 | Afghanistan | 128 | |
| 124 | Marshall Islands | 88 | |
| 125 | Turkmenistan | 84 | |
| 126 | North Korea | 76 | |
| 127 | Kiribati | 35 | |
| 128 | Tuvalu | 24 | |
| 129 | Nauru | 11 | |
| 130 | Timor Leste |
## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(pop/100000, n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Population of the country (hundred thousand)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in GBIF
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(pop/100000, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Population of the country (hundred thousand)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(pop/100000, n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', col= 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Population of the country (hundred thousand)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(area/1000,n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Area of the country (thousand km2)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(area/1000, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Area of the country (thousand km2)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(area/1000, n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Area of the country (thousand km2)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(abs(latitude), n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Absolute decimal latitude of the country\'s centroid',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(abs(latitude), n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Absolute decimal latitude of the country\'s centroid',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>%
mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(abs(latitude), n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='Absolute decimal latitude of the country\'s centroid',
y='Number of users recording on iNaturalist') +
#scale_x_log10() +
scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp/1000, n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP per capita (thousand USD)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp/1000, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP per capita (thousand USD)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>%
mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp/1000, n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP per capita (thousand USD)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp_research, n_records_inat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP of the country dedicated to research (%)',
y='Number of records on iNaturalist (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## records in gbif
ggplot(data_variables %>% mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp_research, n_records_gbif_iNat/1000, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP of the country dedicated to research (%)',
y='Number of iNat records on GBIF (thousand)') +
scale_x_log10() + scale_y_log10() +
theme_bw()## users
ggplot(data_variables %>%
mutate(site_on_iNat = ifelse(!is.na(site_id), 'yes', 'no')),
aes(gdp_research,n_users, label = site_name)) +
geom_point(aes(col=site_on_iNat), size=2, show.legend = F) +
scale_color_manual(values = c('black', '#74AC00')) +
geom_smooth(method='lm', colour = 'black') +
geom_label_repel(aes(fill=site_on_iNat),
colour = "black", #fontface = "bold",
segment.color = 'black',
show.legend = F, max.overlaps= Inf) +
scale_fill_manual(values = c('#F7F7F7', '#74AC00')) +
labs(x='GDP of the country dedicated to research (%)',
y='Number of users recording on iNaturalist') +
scale_x_log10() + scale_y_log10() +
theme_bw()data_regressions <- data_variables %>%
mutate(has_site = ifelse(!is.na(site_name), 1, 0)) %>%
mutate(has_site = as.factor(has_site)) %>%
mutate(n_gbif_inat = n_records_gbif_iNat) %>%
dplyr::select(country_code, n_records_inat, n_gbif_inat, n_users,
area, gdp, gdp_research, pop, latitude, has_site) %>%
filter(!is.na(gdp_research) & !is.na(latitude)) # remove NAsfit_n_records <- lm(n_records_inat ~ area + gdp + gdp_research + pop + latitude + has_site, data=data_regressions)
step_n_records <- stepAIC(fit_n_records, direction = 'both')Start: AIC=2699.2
n_records_inat ~ area + gdp + gdp_research + pop + latitude +
has_site
Df Sum of Sq RSS AIC
- latitude 1 1879465083 179124783878877 2697.2
- pop 1 442967918677 179565872332471 2697.4
- gdp 1 1489740226801 180612644640595 2698.0
<none> 179122904413794 2699.2
- gdp_research 1 4501906687530 183624811101324 2699.6
- has_site 1 36097608129215 215220512543009 2714.6
- area 1 40299431542350 219422335956144 2716.5
Step: AIC=2697.2
n_records_inat ~ area + gdp + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
- pop 1 453430384053 179578214262930 2695.4
- gdp 1 1494507262448 180619291141325 2696.0
<none> 179124783878877 2697.2
- gdp_research 1 4577093451012 183701877329889 2697.6
+ latitude 1 1879465083 179122904413794 2699.2
- area 1 40332782138534 219457566017412 2714.5
- has_site 1 40592890621256 219717674500133 2714.6
Step: AIC=2695.44
n_records_inat ~ area + gdp + gdp_research + has_site
Df Sum of Sq RSS AIC
- gdp 1 1849935347177 181428149610107 2694.4
<none> 179578214262930 2695.4
- gdp_research 1 4234987994738 183813202257668 2695.7
+ pop 1 453430384053 179124783878877 2697.2
+ latitude 1 12341930459 179565872332471 2697.4
- has_site 1 42644595762227 222222810025157 2713.7
- area 1 55159495028943 234737709291873 2718.9
Step: AIC=2694.41
n_records_inat ~ area + gdp_research + has_site
Df Sum of Sq RSS AIC
<none> 181428149610107 2694.4
+ gdp 1 1849935347177 179578214262930 2695.4
+ pop 1 808858468782 180619291141325 2696.0
+ latitude 1 31697541816 181396452068291 2696.4
- gdp_research 1 12303746361332 193731895971439 2698.6
- has_site 1 45743267039413 227171416649520 2713.8
- area 1 53450348971357 234878498581464 2716.9
step_n_records$anova # display results Stepwise Model Path
Analysis of Deviance Table
Initial Model:
n_records_inat ~ area + gdp + gdp_research + pop + latitude +
has_site
Final Model:
n_records_inat ~ area + gdp_research + has_site
Step Df Deviance Resid. Df Resid. Dev AIC
1 88 179122904413794 2699.196
2 - latitude 1 1879465083 89 179124783878877 2697.197
3 - pop 1 453430384053 90 179578214262930 2695.437
4 - gdp 1 1849935347177 91 181428149610107 2694.410
fit_gbif <- lm(n_gbif_inat ~ area + gdp + gdp_research + pop + latitude + has_site, data=data_regressions)
step_gbif <- stepAIC(fit_gbif, direction = 'both')Start: AIC=2564.97
n_gbif_inat ~ area + gdp + gdp_research + pop + latitude + has_site
Df Sum of Sq RSS AIC
- latitude 1 315717312964 43921082329633 2563.7
- gdp 1 666165622016 44271530638684 2564.4
- pop 1 809915653463 44415280670132 2564.7
<none> 43605365016669 2565.0
- gdp_research 1 1129505415664 44734870432333 2565.4
- area 1 8613029999591 52218395016260 2580.1
- has_site 1 8748304304980 52353669321648 2580.3
Step: AIC=2563.66
n_gbif_inat ~ area + gdp + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
- gdp 1 638440605559 44559522935192 2563.0
- pop 1 718375607131 44639457936764 2563.2
<none> 43921082329633 2563.7
- gdp_research 1 1345687709564 45266770039198 2564.5
+ latitude 1 315717312964 43605365016669 2565.0
- has_site 1 8614876196108 52535958525741 2578.7
- area 1 8744991457837 52666073787471 2578.9
Step: AIC=2563.03
n_gbif_inat ~ area + gdp_research + pop + has_site
Df Sum of Sq RSS AIC
<none> 44559522935192 2563.0
- pop 1 1003137153861 45562660089053 2563.1
+ gdp 1 638440605559 43921082329633 2563.7
+ latitude 1 287992296508 44271530638684 2564.4
- gdp_research 1 4133054009908 48692576945101 2569.4
- area 1 8740680470877 53300203406070 2578.0
- has_site 1 9195008827648 53754531762841 2578.8
step_gbif$anova # display results Stepwise Model Path
Analysis of Deviance Table
Initial Model:
n_gbif_inat ~ area + gdp + gdp_research + pop + latitude + has_site
Final Model:
n_gbif_inat ~ area + gdp_research + pop + has_site
Step Df Deviance Resid. Df Resid. Dev AIC
1 88 43605365016669 2564.971
2 - latitude 1 315717312964 89 43921082329633 2563.656
3 - gdp 1 638440605559 90 44559522935192 2563.027
fit_users <- lm(n_users ~ area + gdp + gdp_research + pop + latitude + has_site, data=data_regressions)
step_users <- stepAIC(fit_users, direction = 'both')Start: AIC=1944.29
n_users ~ area + gdp + gdp_research + pop + latitude + has_site
Df Sum of Sq RSS AIC
- pop 1 471448 63396597039 1942.3
- gdp 1 328699880 63724825472 1942.8
<none> 63396125592 1944.3
- latitude 1 1404571530 64800697122 1944.4
- gdp_research 1 2853359159 66249484751 1946.5
- area 1 5298553260 68694678852 1949.9
- has_site 1 8195320985 71591446577 1953.8
Step: AIC=1942.29
n_users ~ area + gdp + gdp_research + latitude + has_site
Df Sum of Sq RSS AIC
- gdp 1 342789826 63739386865 1940.8
<none> 63396597039 1942.3
- latitude 1 1424279387 64820876426 1942.4
+ pop 1 471448 63396125592 1944.3
- gdp_research 1 2902285031 66298882070 1944.5
- area 1 8063769189 71460366229 1951.7
- has_site 1 8288821594 71685418633 1952.0
Step: AIC=1940.8
n_users ~ area + gdp_research + latitude + has_site
Df Sum of Sq RSS AIC
<none> 63739386865 1940.8
- latitude 1 1497676656 65237063521 1941.0
+ gdp 1 342789826 63396597039 1942.3
+ pop 1 14561393 63724825472 1942.8
- gdp_research 1 6321864595 70061251460 1947.8
- area 1 7773614186 71513001051 1949.7
- has_site 1 8742389334 72481776199 1951.0
step_users$anova # display results Stepwise Model Path
Analysis of Deviance Table
Initial Model:
n_users ~ area + gdp + gdp_research + pop + latitude + has_site
Final Model:
n_users ~ area + gdp_research + latitude + has_site
Step Df Deviance Resid. Df Resid. Dev AIC
1 88 63396125592 1944.285
2 - pop 1 471447.8 89 63396597039 1942.286
3 - gdp 1 342789825.6 90 63739386865 1940.798
# n_records_inat ~ area + gdp_research + has_site
# n_gbif_inat ~ area + gdp_research + pop + has_site
# n_users ~ area + gdp_research + latitude + has_site
modelo_n_records <- lm(n_records_inat ~ area + gdp + gdp_research + has_site, data=data_regressions)
modelo_gbif <- lm(n_gbif_inat ~ area + gdp + gdp_research + latitude, data=data_regressions)
modelo_users <- lm(n_users ~ area + gdp_research + latitude + has_site, data=data_regressions)stargazer::stargazer(modelo_n_records,
ci = T, digits=1,
type='html',
title = 'Número de registros en iNaturalist')| Dependent variable: | |
| n_records_inat | |
| area | 0.5*** |
| (0.3, 0.7) | |
| gdp | 6.8 |
| (-7.0, 20.5) | |
| gdp_research | 223,318.7 |
| (-77,117.8, 523,755.2) | |
| has_site1 | 1,740,284.0*** |
| (1,002,479.0, 2,478,089.0) | |
| Constant | -71,688.8 |
| (-509,920.9, 366,543.3) | |
| Observations | 95 |
| R2 | 0.4 |
| Adjusted R2 | 0.4 |
| Residual Std. Error | 1,412,556.0 (df = 90) |
| F Statistic | 16.3*** (df = 4; 90) |
| Note: | p<0.1; p<0.05; p<0.01 |
stargazer::stargazer(modelo_gbif,
ci = T, digits=1,
type='html',
title = 'Cantidad de registros en GBIF')| Dependent variable: | |
| n_gbif_inat | |
| area | 0.2*** |
| (0.1, 0.3) | |
| gdp | 6.5* |
| (-1.0, 14.0) | |
| gdp_research | 128,066.1 |
| (-38,268.1, 294,400.3) | |
| latitude | -1,002.5 |
| (-3,522.5, 1,517.5) | |
| Constant | 18,779.6 |
| (-221,197.1, 258,756.2) | |
| Observations | 95 |
| R2 | 0.3 |
| Adjusted R2 | 0.2 |
| Residual Std. Error | 773,090.8 (df = 90) |
| F Statistic | 7.8*** (df = 4; 90) |
| Note: | p<0.1; p<0.05; p<0.01 |
stargazer::stargazer(modelo_users,
ci = T, digits=1,
type='html',
title = 'Cantidad de usuarios en iNat')| Dependent variable: | |
| n_users | |
| area | 0.01*** |
| (0.002, 0.01) | |
| gdp_research | 7,115.4*** |
| (2,447.6, 11,783.1) | |
| latitude | -68.4 |
| (-160.5, 23.8) | |
| has_site1 | 26,347.7*** |
| (11,649.7, 41,045.7) | |
| Constant | 5,051.0 |
| (-2,920.3, 13,022.4) | |
| Observations | 95 |
| R2 | 0.3 |
| Adjusted R2 | 0.3 |
| Residual Std. Error | 26,612.3 (df = 90) |
| F Statistic | 11.1*** (df = 4; 90) |
| Note: | p<0.1; p<0.05; p<0.01 |